Jingchen Hu

405 total citations
22 papers, 131 citations indexed

About

Jingchen Hu is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Jingchen Hu has authored 22 papers receiving a total of 131 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Statistics and Probability, 12 papers in Artificial Intelligence and 7 papers in Management Science and Operations Research. Recurrent topics in Jingchen Hu's work include Statistical Methods and Bayesian Inference (8 papers), Statistics Education and Methodologies (8 papers) and Privacy-Preserving Technologies in Data (5 papers). Jingchen Hu is often cited by papers focused on Statistical Methods and Bayesian Inference (8 papers), Statistics Education and Methodologies (8 papers) and Privacy-Preserving Technologies in Data (5 papers). Jingchen Hu collaborates with scholars based in United States, United Kingdom and Germany. Jingchen Hu's co-authors include Jim Albert, Jerome P. Reiter, Quanli Wang, Jörg Drechsler, Terrance D. Savitsky, Matthew R. Williams, Matthew J. Schneider, Shawn Mankad, Colin Rundel and Allan J. Rossman and has published in prestigious journals such as Expert Systems with Applications, The American Statistician and Journal of the Royal Statistical Society Series A (Statistics in Society).

In The Last Decade

Jingchen Hu

21 papers receiving 122 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jingchen Hu United States 7 67 59 28 20 10 22 131
Jiannan Lu United States 7 18 0.3× 136 2.3× 34 1.2× 9 0.5× 9 0.9× 17 196
Xiaojie Mao United States 6 40 0.6× 28 0.5× 35 1.3× 13 0.7× 3 0.3× 13 141
Ashudeep Singh United States 4 57 0.9× 10 0.2× 50 1.8× 8 0.4× 14 1.4× 7 103
Katherine A. Keith United States 5 101 1.5× 8 0.1× 9 0.3× 11 0.6× 5 0.5× 9 250
Samuel Ventura United States 5 19 0.3× 10 0.2× 30 1.1× 3 0.1× 1 0.1× 8 126
Marcel Neunhoeffer Germany 5 19 0.3× 9 0.2× 5 0.2× 14 0.7× 2 0.2× 9 103
Vijay Keswani United States 3 86 1.3× 5 0.1× 8 0.3× 11 0.6× 8 0.8× 10 125
Manfred Borovcnik Austria 7 16 0.2× 182 3.1× 10 0.4× 11 0.6× 3 0.3× 27 235
F. Jay Breyer United States 5 134 2.0× 12 0.2× 37 1.3× 2 0.1× 32 3.2× 10 272
Seth Neel United States 7 92 1.4× 4 0.1× 19 0.7× 14 0.7× 13 1.3× 12 119

Countries citing papers authored by Jingchen Hu

Since Specialization
Citations

This map shows the geographic impact of Jingchen Hu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jingchen Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingchen Hu more than expected).

Fields of papers citing papers by Jingchen Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jingchen Hu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jingchen Hu. The network helps show where Jingchen Hu may publish in the future.

Co-authorship network of co-authors of Jingchen Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Jingchen Hu. A scholar is included among the top collaborators of Jingchen Hu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jingchen Hu. Jingchen Hu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hu, Jingchen, et al.. (2023). Introducing Variational Inference in Statistics and Data Science Curriculum. The American Statistician. 78(3). 359–367. 1 indexed citations
2.
Hu, Jingchen, et al.. (2023). Advancing microdata privacy protection: A review of synthetic data methods. Wiley Interdisciplinary Reviews Computational Statistics. 16(1). 2 indexed citations
3.
Hu, Jingchen, Matthew R. Williams, & Terrance D. Savitsky. (2023). Mechanisms for Global Differential Privacy under Bayesian Data Synthesis. Statistica Sinica. 1 indexed citations
4.
Hu, Jingchen, et al.. (2022). The Current State of Undergraduate Bayesian Education and Recommendations for the Future. The American Statistician. 76(4). 405–413. 7 indexed citations
5.
6.
Schneider, Matthew J., et al.. (2022). Protecting the anonymity of online users through Bayesian data synthesis. Expert Systems with Applications. 216. 119409–119409. 2 indexed citations
7.
Hu, Jingchen, Terrance D. Savitsky, & Matthew R. Williams. (2022). Private Tabular Survey Data Products through Synthetic Microdata Generation. Journal of Survey Statistics and Methodology. 10(3). 720–752. 6 indexed citations
8.
Hu, Jingchen, et al.. (2022). Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy. Journal of Survey Statistics and Methodology. 10(3). 688–719.
9.
Hu, Jingchen, et al.. (2021). Multiple Imputation and Synthetic Data Generation with NPBayesImputeCat. The R Journal. 13(2). 25–25. 4 indexed citations
10.
Hu, Jingchen, Terrance D. Savitsky, & Matthew R. Williams. (2020). Risk-Weighted Data Synthesizers for Microdata Dissemination. CHANCE. 33(4). 29–36. 1 indexed citations
11.
Albert, Jim & Jingchen Hu. (2020). Bayesian Computing in the Undergraduate Statistics Curriculum. Journal of Statistics Education. 28(3). 236–247. 6 indexed citations
12.
Drechsler, Jörg & Jingchen Hu. (2020). Synthesizing Geocodes to Facilitate Access to Detailed Geographical Information in Large-Scale Administrative Data. Journal of Survey Statistics and Methodology. 9(3). 523–548. 9 indexed citations
13.
Hu, Jingchen. (2020). A Bayesian Statistics Course for Undergraduates: Bayesian Thinking, Computing, and Research. Journal of Statistics Education. 28(3). 229–235. 11 indexed citations
14.
Rundel, Colin, et al.. (2020). Teaching an Undergraduate Course in Bayesian Statistics: A Panel Discussion. Journal of Statistics Education. 28(3). 251–261. 6 indexed citations
15.
Savitsky, Terrance D., Matthew R. Williams, & Jingchen Hu. (2019). Bayesian Pseudo Posterior Mechanism under Differential Privacy. arXiv (Cornell University). 2 indexed citations
16.
Hu, Jingchen. (2019). Bayesian Estimation of Attribute and Identification Disclosure Risks in Synthetic Data.. 12. 61–89. 2 indexed citations
17.
18.
Hu, Jingchen, et al.. (2018). Bayesian Non-Parametric Generation Of Fully Synthetic Multivariate Categorical Data in the Presence of Structural Zeros. Journal of the Royal Statistical Society Series A (Statistics in Society). 181(3). 635–647. 9 indexed citations
19.
Hu, Jingchen, Jerome P. Reiter, & Quanli Wang. (2017). Dirichlet Process Mixture Models for Modeling and Generating Synthetic Versions of Nested Categorical Data. Bayesian Analysis. 13(1). 24 indexed citations
20.
Hu, Jingchen, Robin Mitra, & Jerome P. Reiter. (2013). Are Independent Parameter Draws Necessary for Multiple Imputation?. The American Statistician. 67(3). 143–149. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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